Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy.

Shallow landslides represent potentially damaging processes in mountain areas worldwide. These geomorphic processes are usually caused by an interplay of predisposing, preparatory, and triggering environmental factors. At regional scales, data-driven methods have been used to model shallow landslides by addressing the spatial and temporal components separately. So far, few studies have explored the integration of space and time for landslide prediction. This research leverages generalized additive mixed models to develop an integrated approach to model shallow landslides in space and time. We built upon data on precipitation-induced landslide records from 2000 to 2020 in South Tyrol, Italy (7400 km2). The slope unit-based model predicts landslide occurrence as a function of static and dynamic factors while seasonal effects are incorporated. The model also accounts for spatial and temporal biases inherent in the underlying landslide data. We validated the resulting predictions through a suite of cross-validation techniques, obtaining consistent performance scores above 0.85. The analyses revealed that the best-performing model combines static ground conditions and two precipitation time windows: a short-term cumulative precipitation of 2 days before the landslide event and a medium-term cumulative precipitation of 14 days. We demonstrated the model's predictive capabilities by predicting the dynamic landslide probabilities over historical data associated with a heavy precipitation event on August 4th and August 5th, 2016, and hypothetical non-spatially explicit precipitation (what-if) scenarios. The novel approach shows the potential to integrate static and dynamic landslide factors for large areas, accounting for the underlying data structure and data limitations.

Evaluation of tectonically enhanced radon in fault zones by quantification of the radon activity index.

This work highlights the importance of the Geogenic Radon Potential (GRP) component originated by degassing processes in fault zones. This Tectonically Enhanced Radon (TER) can increase radon concentration in soil gas and the inflow of radon in the buildings (Indoor Radon Concentrations, IRC). Although tectonically related radon enhancement is known in areas characterised by active faults, few studies have investigated radon migration processes in non-active fault zones. The Pusteria Valley (Bolzano, north-eastern Italy) represents an ideal geological setting to study the role of a non-seismic fault system in enhancing the geogenic radon. Here, most of the municipalities are characterised by high IRC. We performed soil gas surveys in three of these municipalities located along a wide section of the non-seismic Pusteria fault system characterised by a dense network of faults and fractures. Results highlight the presence of high Rn concentrations (up to 800 kBq·m-3) with anisotropic spatial patterns oriented along the main strike of the fault system. We calculated a Radon Activity Index (RAI) along north-south profiles across the Pusteria fault system and found that TER is linked to high fault geochemical activities. This evidence confirms that TER constitutes a significant component of GRP also along non-seismic faults.

Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling - Benefits of exploring landslide data collection effects.

Data-driven landslide susceptibility models formally integrate spatial landslide information with explanatory environmental variables that describe predisposing factors of slope instability. Well-performing models are commonly utilized to identify landslide-prone terrain or to understand the causes of slope instability. In most cases, however, the available landslide data is affected by spatial biases (e.g. underrepresentation of landslides far from infrastructure or in forests) and does therefore not perfectly represent the spatial distribution of past slope instabilities. Literature shows that implications of such data flaws are frequently ignored. This study was built upon landslide information that systematically relates to damage-causing and infrastructure-threatening events in South Tyrol, Italy (7400 km2). The created models represent three conceptually different strategies to deal with biased landslide information. The aims were to demonstrate why an inference of geomorphic causation from apparently well-performing models is invalid under common landslide data bias conditions (Model 1), to test a novel bias-adjustment approach (Model 2) and to exploit the underlying data bias to model areas likely affected by potentially damaging landslides (Model 3; intervention index), instead of landslide susceptibility. The study offers a novel perspective on how biases in landslide data can be considered within data-driven models by focusing not only on the process under investigation (landsliding), but also on the circumstances that led to the registration of landslide information (data collection effects). The results were evaluated in terms of statistical relationships, variable importance, predictive performance, and geomorphic plausibility. The results revealed that none of the models reflected landslide susceptibility. Despite partly high predictive performances, the models were unable to create geomorphically plausible spatial predictions. The impact-oriented intervention index, however, enabled to identify damage-causing landslides with high accuracy. We conclude that the frequent practice of inferring geomorphic causation from well-performing models without accounting for data limitations is invalid.

Unexpected response of high Alpine Lake waters to climate warming.

Over the past two decades, we have observed a substantial rise in solute concentration at two remote high mountain lakes in catchments of metamorphic rocks in the European Alps. At Rasass See, the electrical conductivity increased 18-fold. Unexpectedly high nickel concentrations at Rasass See, which exceeded the limit in drinking water by more than 1 order of magnitude, cannot be related to catchment geology. We attribute these changes in lake water quality to solute release from the ice of an active rock glacier in the catchment as a response to climate warming. Similar processes occurred at the higher elevation lake Schwarzsee ob Sölden, where electrical conductivity has risen 3-fold during the past two decades.